{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21a2ec7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-11T18:23:17.485600Z",
     "iopub.status.busy": "2023-10-11T18:23:17.485600Z",
     "iopub.status.idle": "2023-10-11T18:23:17.497085Z",
     "shell.execute_reply": "2023-10-11T18:23:17.496110Z",
     "shell.execute_reply.started": "2023-10-11T18:23:17.485600Z"
    }
   },
   "source": [
    "This notebook replicates Figures 2-5 of the paper.\n",
    "- It requires the file `data_exports/facebook_population.csv` which must be created by running `allcountries_fbpopulation_datacleaning.ipynb`.\n",
    "- It also requires the following files created by previously running the supplied `R` scripts:\n",
    "    - `data_exports/policy_mexico_f4.csv`\n",
    "    - `data_exports/demos_mexico_fig2a_unadjusted.csv`\n",
    "    - `data_exports/demos_mexico_fig2b_weighted.csv`\n",
    "    - `data_exports/nonresponse_mexico_attrition~demos.csv`\n",
    "    - ---\n",
    "    - `data_exports/polbeh_kenya_table_replicate.csv`\n",
    "    - `data_exports/demos_kenya_facebook_table_replicate.csv`\n",
    "    - `data_exports/kenya_adclicker_tab.csv`\n",
    "    - `data_exports/kenya_attrition_regtab_replicate.csv`\n",
    "    - ---\n",
    "    - `data_exports/politicalparticipation_indonesia_wave1.csv`\n",
    "    - `data_exports/demographics_indonesia.csv`\n",
    "    - `data_exports/nonresponse_indonesia_attrition~demos.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9307f85a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:47.821380Z",
     "iopub.status.busy": "2024-11-21T01:04:47.821380Z",
     "iopub.status.idle": "2024-11-21T01:04:48.838540Z",
     "shell.execute_reply": "2024-11-21T01:04:48.838540Z",
     "shell.execute_reply.started": "2024-11-21T01:04:47.821380Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1.0\n",
      "3.2.2\n",
      "1.19.2\n",
      "3.6.10 (default, Mar  5 2020, 10:17:47) [MSC v.1900 64 bit (AMD64)]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.lines as mlines\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:\\\\Program Files\\\\MiKTeX 2.9\\\\miktex\\\\bin\\\\x64\\\\'\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:\\\\Program Files\\\\gs\\\\gs9.52\\\\bin\\\\'\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "\n",
    "print(pd.__version__)\n",
    "print(matplotlib.__version__)\n",
    "print(np.__version__)\n",
    "print(sys.version) # Python version"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf527a6",
   "metadata": {},
   "source": [
    "# Main analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db6ba85",
   "metadata": {},
   "source": [
    "## Read in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0551de7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-11T17:10:08.723425Z",
     "iopub.status.busy": "2023-10-11T17:10:08.723425Z",
     "iopub.status.idle": "2023-10-11T17:10:08.771851Z",
     "shell.execute_reply": "2023-10-11T17:10:08.770568Z",
     "shell.execute_reply.started": "2023-10-11T17:10:08.723425Z"
    }
   },
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "abbd35f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:48.838540Z",
     "iopub.status.busy": "2024-11-21T01:04:48.838540Z",
     "iopub.status.idle": "2024-11-21T01:04:48.911876Z",
     "shell.execute_reply": "2024-11-21T01:04:48.911876Z",
     "shell.execute_reply.started": "2024-11-21T01:04:48.838540Z"
    }
   },
   "outputs": [],
   "source": [
    "# Mexico attitudes, demographics, attrition\n",
    "mex_att           = pd.read_csv(\"data_exports/fig4_policy_mexico.csv\",                  \n",
    "                                usecols = ['estimate', 'std.error', 'term', 'model'] )\n",
    "mex_demo          = pd.read_csv(\"data_exports/fig2_demos_mexico_unadjusted.csv\", \n",
    "                                usecols = ['estimate', 'std.error', 'term', 'model'])\n",
    "mex_demo_weighted = pd.read_csv(\"data_exports/fig2_demos_mexico_weighted.csv\",   \n",
    "                                usecols = ['estimate', 'std.error', 'term', 'model'])\n",
    "mexico_regs = pd.read_csv(\"data_exports/fig3_attritionreg_mexico.csv\")\n",
    "\n",
    "\n",
    "# Kenya attitudes, demographics, attrition\n",
    "kenya_att  = pd.read_csv(\"data_exports/fig4_behavior_kenya.csv\", \n",
    "                         usecols = ['mean', 'se', 'variable', 'sample', 'ci.low', 'ci.high'])\n",
    "kenya_demo = pd.read_csv(\"data_exports/fig2_demos_kenya.csv\", \n",
    "                         usecols = ['mean', 'se', 'variable', 'sample', 'ci.low', 'ci.high'])\n",
    "kenya_ad = pd.read_csv(\"data_exports/figS1_kenya_adclicker_tab.csv\", \n",
    "                         usecols = ['mean', 'se', 'variable', 'sample', 'ci.low', 'ci.high', 'sample'])\n",
    "kenya_regs = pd.read_csv(\"data_exports/fig3_attritionreg_kenya.csv\")\n",
    "\n",
    "# Indonesia attitudes, demographics, attrition\n",
    "ind_att          = pd.read_csv(\"data_exports/fig4-5_indonesia_policy_behavior.csv\", \n",
    "                               usecols = ['estimate', 'std.error', 'term', 'sample'])\n",
    "ind_demo          = pd.read_csv(\"data_exports/fig2_demos_indonesia.csv\", \n",
    "                                usecols = ['estimate', 'std.error', 'term', 'sample'])\n",
    "indonesia_regs = pd.read_csv(\"data_exports/fig3_attritionreg_indonesia.csv\")\n",
    "\n",
    "# All countries - Facebook audience estimates\n",
    "fbpop = pd.read_csv(\"data_exports/fig2_facebook_population.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "678d3334",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-11T17:10:08.723425Z",
     "iopub.status.busy": "2023-10-11T17:10:08.723425Z",
     "iopub.status.idle": "2023-10-11T17:10:08.771851Z",
     "shell.execute_reply": "2023-10-11T17:10:08.770568Z",
     "shell.execute_reply.started": "2023-10-11T17:10:08.723425Z"
    }
   },
   "source": [
    "### Standardize column naming convention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65628b1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:48.914571Z",
     "iopub.status.busy": "2024-11-21T01:04:48.914571Z",
     "iopub.status.idle": "2024-11-21T01:04:48.936619Z",
     "shell.execute_reply": "2024-11-21T01:04:48.935884Z",
     "shell.execute_reply.started": "2024-11-21T01:04:48.914571Z"
    }
   },
   "outputs": [],
   "source": [
    "## Mexico\n",
    "mex_att.rename(          columns = {'estimate': 'mean', 'std.error': 'se', 'term':'variable', 'model': 'source'}, inplace=True)\n",
    "mex_demo.rename(         columns = {'estimate': 'mean', 'std.error': 'se', 'term':'variable', 'model': 'source'}, inplace=True)\n",
    "mex_demo_weighted.rename(columns = {'estimate': 'mean', 'std.error': 'se', 'term':'variable', 'model': 'source'}, inplace=True)\n",
    "\n",
    "## Kenya\n",
    "kenya_att.columns  = [i.lower() for i in kenya_att.columns]\n",
    "kenya_demo.columns = [i.lower() for i in kenya_demo.columns]\n",
    "kenya_ad.columns = [i.lower() for i in kenya_ad.columns]\n",
    "\n",
    "kenya_att.rename(columns={\"sample\": 'source'}, inplace=True)\n",
    "kenya_demo.rename(columns={\"sample\": 'source'}, inplace=True)\n",
    "kenya_ad.rename(columns={\"sample\": 'source'}, inplace=True)\n",
    "kenya_regs.rename(columns={\"Unnamed: 0\": 'term', 'std. error': 'std.error'}, inplace=True)\n",
    "\n",
    "## Indonesia\n",
    "ind_att.rename(          columns = {'estimate': 'mean', 'std.error': 'se', 'term':'variable', 'sample': 'source'}, inplace=True)\n",
    "ind_demo.rename(         columns = {'estimate': 'mean', 'std.error': 'se',  'term':'variable', 'sample': 'source'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e814d3",
   "metadata": {},
   "source": [
    "### Recode data source names to be consistent across datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5752e62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:48.938443Z",
     "iopub.status.busy": "2024-11-21T01:04:48.938443Z",
     "iopub.status.idle": "2024-11-21T01:04:49.027541Z",
     "shell.execute_reply": "2024-11-21T01:04:49.027541Z",
     "shell.execute_reply.started": "2024-11-21T01:04:48.938443Z"
    }
   },
   "outputs": [],
   "source": [
    "## Mexico\n",
    "mex_demo_weighted['source'] = mex_demo_weighted['source'] + \" (weighted)\"\n",
    "mex_att['source'] = mex_att['source'].replace({\n",
    "                            \"Facebook (unweighted)\": \"Facebook Sample\",\n",
    "                            \"Facebook (weighted)\"  : \"Facebook Sample (weighted)\"})\n",
    "\n",
    "\n",
    "## Kenya\n",
    "kenya_demo['source'] = kenya_demo['source'].replace({\n",
    "                            \"AB Survey\"     : \"Afrobarometer\",\n",
    "                            'AB (weighted)' : 'Afrobarometer (weighted)' })\n",
    "\n",
    "\n",
    "kenya_ad['source'] = kenya_ad['source'].replace({\n",
    "                           'Facebook ad clickers': 'Facebook (ad clickers)'})\n",
    "\n",
    "\n",
    "kenya_att['source'] = kenya_att['source'].replace({\n",
    "                            'Afrobatometer (weighted)' : 'Afrobarometer (weighted)',})\n",
    "\n",
    "\n",
    "## Indonesia\n",
    "ind_demo['source'] = ind_demo['source'].replace({\n",
    "                            \"Facebook (weighted)\"  : \"Facebook Sample (weighted)\",\n",
    "                            \"Facebook (unweighted)\": \"Facebook Sample\",\n",
    "                            \"Dynata (unweighted)\"  : \"Dynata\"})\n",
    "\n",
    "ind_att['source'] = ind_att['source'].replace({\n",
    "                            \"Facebook (weighted)\"  : \"Facebook Sample (weighted)\",\n",
    "                            \"Facebook (unweighted)\": \"Facebook Sample\" ,\n",
    "                            \"Dynata (unweighted)\"  : \"Dynata\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8e726b",
   "metadata": {},
   "source": [
    "### Merge and reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d1e6820",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.027541Z",
     "iopub.status.busy": "2024-11-21T01:04:49.027541Z",
     "iopub.status.idle": "2024-11-21T01:04:49.081470Z",
     "shell.execute_reply": "2024-11-21T01:04:49.079842Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.027541Z"
    }
   },
   "outputs": [],
   "source": [
    "mex_demo = pd.concat([mex_demo, mex_demo_weighted[mex_demo_weighted['source'] == 'Facebook Sample (weighted)' ]])\n",
    "kenya_demo = pd.concat([kenya_demo, kenya_ad])\n",
    "\n",
    "# Add facebook populations\n",
    "mex_demo = mex_demo[mex_demo['source']!= \"Facebook Population\"]\n",
    "mex_demo = pd.concat([mex_demo, fbpop[fbpop.country==\"Mexico\"].drop(columns=['country'])])\n",
    "kenya_demo = pd.concat([kenya_demo, fbpop[fbpop.country==\"Kenya\"].drop(columns=['country'])])\n",
    "ind_demo = pd.concat([ind_demo, fbpop[fbpop.country==\"Indonesia\"].drop(columns=['country'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2780647e",
   "metadata": {},
   "source": [
    "### Recode variables so that the names are proper for the plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "caeffcac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.081470Z",
     "iopub.status.busy": "2024-11-21T01:04:49.081470Z",
     "iopub.status.idle": "2024-11-21T01:04:49.122081Z",
     "shell.execute_reply": "2024-11-21T01:04:49.122081Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.081470Z"
    }
   },
   "outputs": [],
   "source": [
    "kenya_ad['variable'] = kenya_ad['variable'].replace({\n",
    "                           'ad_female': 'Female',\n",
    "                           'ad_old'   : '32+'})\n",
    "\n",
    "kenya_demo['variable'] = kenya_demo['variable'].replace(\"No Religion\", \"No religion\")\n",
    "\n",
    "kenya_demo['variable'] =kenya_demo['variable'].str.title().replace({\n",
    "                'Collegeplus'                      : \"College degree or higher\",\n",
    "                'Secondary'                        : \"Secondary degree\",\n",
    "                'Nonsec'                           : \"Did not complete secondary school\",\n",
    "                'College Degree Or Higher'         : 'College degree or higher', \n",
    "                'Did Not Complete Secondary School': 'Did not complete secondary school',\n",
    "                'Secondary Degree'                 : 'Secondary degree', \n",
    "                'Unspecified Education'            : 'Unspeicfied education',\n",
    "                'Postsec'   :'Post-secondary or above', \n",
    "                'Secorless' : 'Secondary schl or less',\n",
    "                'Norel'     : 'No religion', \n",
    "                'Age4'      :'60+',  \n",
    "                'Age3'      :'50-59', \n",
    "                'Age2'      :'30-49',\n",
    "                'Age1'      :'18-29', \n",
    "                        })\n",
    "\n",
    "kenya_att['variable'] = kenya_att['variable'].replace({\n",
    "                'attendmtg'  :'Attended meeting', \n",
    "                'protest'    : 'Protested',\n",
    "                'voted'      :'Voted in 2017 election',\n",
    "                \"approvepres\": \"Approve of President\",\n",
    "                'freetosay'  :'Free to speak', \n",
    "                'closeparty' :'Close to political party',\n",
    "                'raise'      :'Raised an issue', })\n",
    "\n",
    "\n",
    "mex_demo['variable']          = mex_demo['variable'].replace({\"18to29\":\"18-29\", \"30to49\":\"30-49\", \"50to59\":\"50-59\"})\n",
    "mex_demo_weighted['variable'] = mex_demo_weighted['variable'].replace({\"18to29\":\"18-29\", \"30to49\":\"30-49\", \"50to59\":\"50-59\"})\n",
    "\n",
    "indonesia_regs['term'] = indonesia_regs['term'].replace({\"fb_agegroup30-49\":\"Age 30-49\",\"fb_agegroup50-59\":\"Age 50-59\",\"fb_agegroup60+\":\"Age 60+\",\"fb_genderMale\":\"Male\"})\n",
    "mexico_regs['term']    = mexico_regs['term'].replace({\"ageFB3049\":\"Age 30-49\",\"ageFB5059\":\"Age 50-59\",\"ageFB60\":\"Age 60+\",\"genderFBM\":\"Male\"})\n",
    "kenya_regs['term']     = kenya_regs['term'].replace({\"ad_old\": \"Age 32+\", \"ad_male\":\"Male\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5ebd6a",
   "metadata": {},
   "source": [
    "### Index and reshape for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fd14ee6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.126215Z",
     "iopub.status.busy": "2024-11-21T01:04:49.126215Z",
     "iopub.status.idle": "2024-11-21T01:04:49.195419Z",
     "shell.execute_reply": "2024-11-21T01:04:49.195419Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.126215Z"
    }
   },
   "outputs": [],
   "source": [
    "## Kenya\n",
    "kenya_att         = kenya_att.set_index(['source', 'variable'])[['mean', 'se']].unstack(level='source')\n",
    "kenya_att.columns = kenya_att.columns.swaplevel()\n",
    "\n",
    "kenya_demo         = kenya_demo.set_index(['source', 'variable'])[['mean', 'se']].unstack(level='source')\n",
    "kenya_demo.columns = kenya_demo.columns.swaplevel()\n",
    "\n",
    "kenya_regs.set_index('term', inplace=True)\n",
    "\n",
    "## Mexico\n",
    "mex_att          = mex_att.set_index(['source', 'variable'])[['mean', 'se']].unstack(level='source')\n",
    "mex_att.columns  = mex_att.columns.swaplevel()\n",
    "\n",
    "mex_demo.dropna(subset=['variable'], inplace=True) # Drop random blank columns\n",
    "\n",
    "mex_demo         = mex_demo.set_index(['source', 'variable'])[['mean', 'se']].unstack(level='source')\n",
    "mex_demo.columns = mex_demo.columns.swaplevel()\n",
    "\n",
    "mexico_regs.set_index('term', inplace=True)\n",
    "\n",
    "\n",
    "## Indonesia\n",
    "ind_att          = ind_att.set_index(['source', 'variable'])[['mean', 'se']].unstack(level='source')\n",
    "ind_att.columns  = ind_att.columns.swaplevel()\n",
    "\n",
    "ind_demo         = ind_demo.set_index(['source', 'variable'])[['mean', 'se']].unstack(level='source')\n",
    "ind_demo.columns = ind_demo.columns.swaplevel()\n",
    "\n",
    "indonesia_regs.set_index('term', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976d67b2",
   "metadata": {},
   "source": [
    "### Manually add Kenya election results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a5942f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.195419Z",
     "iopub.status.busy": "2024-11-21T01:04:49.195419Z",
     "iopub.status.idle": "2024-11-21T01:04:49.213861Z",
     "shell.execute_reply": "2024-11-21T01:04:49.213258Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.195419Z"
    }
   },
   "outputs": [],
   "source": [
    "kenya_att[('Election results', 'mean')] = np.nan\n",
    "kenya_att[('Election results', 'se')] = np.nan\n",
    "kenya_att.loc[\"Voted in 2017 election\", ('Election results', 'mean')] = 0.795"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20f9642e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.217889Z",
     "iopub.status.busy": "2024-11-21T01:04:49.217889Z",
     "iopub.status.idle": "2024-11-21T01:04:49.263543Z",
     "shell.execute_reply": "2024-11-21T01:04:49.263543Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.217889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>estimate</th>\n",
       "      <th>std.error</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p.value</th>\n",
       "      <th>conf.low</th>\n",
       "      <th>conf.high</th>\n",
       "      <th>df</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(Intercept)</th>\n",
       "      <td>0.501757</td>\n",
       "      <td>0.011467</td>\n",
       "      <td>43.756125</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.479279</td>\n",
       "      <td>0.524235</td>\n",
       "      <td>9616</td>\n",
       "      <td>attrition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.015044</td>\n",
       "      <td>0.010185</td>\n",
       "      <td>1.477098</td>\n",
       "      <td>1.396821e-01</td>\n",
       "      <td>-0.004920</td>\n",
       "      <td>0.035009</td>\n",
       "      <td>9616</td>\n",
       "      <td>attrition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age 30-49</th>\n",
       "      <td>-0.065801</td>\n",
       "      <td>0.014478</td>\n",
       "      <td>-4.544817</td>\n",
       "      <td>5.565407e-06</td>\n",
       "      <td>-0.094182</td>\n",
       "      <td>-0.037421</td>\n",
       "      <td>9616</td>\n",
       "      <td>attrition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age 50-59</th>\n",
       "      <td>-0.074844</td>\n",
       "      <td>0.014371</td>\n",
       "      <td>-5.207859</td>\n",
       "      <td>1.949841e-07</td>\n",
       "      <td>-0.103015</td>\n",
       "      <td>-0.046673</td>\n",
       "      <td>9616</td>\n",
       "      <td>attrition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age 60+</th>\n",
       "      <td>-0.049179</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>-3.475411</td>\n",
       "      <td>5.123217e-04</td>\n",
       "      <td>-0.076918</td>\n",
       "      <td>-0.021441</td>\n",
       "      <td>9616</td>\n",
       "      <td>attrition</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             estimate  std.error  statistic       p.value  conf.low  \\\n",
       "term                                                                  \n",
       "(Intercept)  0.501757   0.011467  43.756125  0.000000e+00  0.479279   \n",
       "Male         0.015044   0.010185   1.477098  1.396821e-01 -0.004920   \n",
       "Age 30-49   -0.065801   0.014478  -4.544817  5.565407e-06 -0.094182   \n",
       "Age 50-59   -0.074844   0.014371  -5.207859  1.949841e-07 -0.103015   \n",
       "Age 60+     -0.049179   0.014151  -3.475411  5.123217e-04 -0.076918   \n",
       "\n",
       "             conf.high    df    outcome  \n",
       "term                                     \n",
       "(Intercept)   0.524235  9616  attrition  \n",
       "Male          0.035009  9616  attrition  \n",
       "Age 30-49    -0.037421  9616  attrition  \n",
       "Age 50-59    -0.046673  9616  attrition  \n",
       "Age 60+      -0.021441  9616  attrition  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mexico_regs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6671a64d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.263543Z",
     "iopub.status.busy": "2024-11-21T01:04:49.263543Z",
     "iopub.status.idle": "2024-11-21T01:04:49.279505Z",
     "shell.execute_reply": "2024-11-21T01:04:49.279505Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.263543Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = mexico_regs.loc[['Male', 'Age 60+', 'Age 50-59', 'Age 30-49', ]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1987912d",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b720960d",
   "metadata": {},
   "source": [
    "### Define plot styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b6fbb32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.279505Z",
     "iopub.status.busy": "2024-11-21T01:04:49.279505Z",
     "iopub.status.idle": "2024-11-21T01:04:49.296034Z",
     "shell.execute_reply": "2024-11-21T01:04:49.296034Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.279505Z"
    }
   },
   "outputs": [],
   "source": [
    "styles_dictionary = {\n",
    "'Facebook Sample'            : ['dodgerblue', 's', 'none'],\n",
    "'Facebook Sample (weighted)' : ['dodgerblue', 's', 'full'],\n",
    "'Facebook Population'        : ['navy', 's', 'full'],\n",
    "'Facebook (ad clickers)'     : ['c', 's', 'left'],\n",
    "'LAPOP'                      : ['tab:red', '^', 'none'],\n",
    "'Afrobarometer'              : ['tab:red', '^', 'none'],\n",
    "'Asiabarometer'              : ['tab:red', '^', 'none'],\n",
    "'LAPOP/Afrobarometer/Asiabarometer': ['tab:red', '^', 'none'],\n",
    "'Afrobarometer/Asiabarometer': ['tab:red', '^', 'none'],\n",
    "'LAPOP/Asiabarometer'        : ['tab:red', '^', 'none'],\n",
    "'Afrobarometer (weighted)'   : ['tab:red', '^', 'full'],\n",
    "'Census'                     : ['goldenrod', 'X', 'full'],\n",
    "'Dynata'                     : ['purple', 'o', 'none'],\n",
    "'Dynata (weighted)'          : ['purple', 'o', 'full'],\n",
    "'Election results'           : ['green', 'P', 'full']}\n",
    "\n",
    "colors = dict((k[0], k[1][0]) for k in styles_dictionary.items())\n",
    "styles = dict((k[0], k[1][1]) for k in styles_dictionary.items())\n",
    "fill_styles = dict((k[0], k[1][2]) for k in styles_dictionary.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0688096",
   "metadata": {},
   "source": [
    "### FIGURE 2: Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de70e8dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.300793Z",
     "iopub.status.busy": "2024-11-21T01:04:49.300793Z",
     "iopub.status.idle": "2024-11-21T01:04:49.324016Z",
     "shell.execute_reply": "2024-11-21T01:04:49.321618Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.300793Z"
    }
   },
   "outputs": [],
   "source": [
    "# Save the data for Figure 2\n",
    "#mex_demo.to_csv(\"data_exports/fig2_mexico.csv\")\n",
    "#kenya_demo.to_csv(\"data_exports/fig2_kenya.csv\")\n",
    "#ind_demo.to_csv(\"data_exports/fig2_ind.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bec64a5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:49.328251Z",
     "iopub.status.busy": "2024-11-21T01:04:49.327200Z",
     "iopub.status.idle": "2024-11-21T01:04:59.238569Z",
     "shell.execute_reply": "2024-11-21T01:04:59.238569Z",
     "shell.execute_reply.started": "2024-11-21T01:04:49.328251Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3, figsize=[18,10])\n",
    "\n",
    "########## KENYA #############################################################################################\n",
    "i=-0.35\n",
    "\n",
    "for var in ['Facebook Sample',\n",
    "            'Facebook Sample (weighted)', \n",
    "            'Facebook Population',\n",
    "            'Afrobarometer', \n",
    "            'Census']:    \n",
    "\n",
    "    plot_df = kenya_demo.loc[['Urban' , \n",
    "                              'Kikuyu', 'Luo',  \n",
    "                              'No religion',  'Muslim',  'Christian',\n",
    "                              'Did not complete secondary school', 'Secondary degree', 'College degree or higher',\n",
    "                              '60+',  '50-59', '30-49', '18-29', \n",
    "                              'Female' ]][var].reset_index()\n",
    "    \n",
    "    ax[1].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8,\n",
    "                 label=var.replace(\"Afrobarometer\", \"LAPOP/Afrobarometer/Asian Barometer\"))\n",
    "    i+=0.15\n",
    "    \n",
    "    \n",
    "ax[1].set_yticks(range(0, plot_df.shape[0]))\n",
    "ax[1].set_yticklabels(plot_df.variable.str.replace(\"schl\", \"school\")\n",
    "                      .replace(\"Did not complete secondary school\", \"Didn't complete \\nsecondary\")\n",
    "                      .replace(\"College degree or higher\", \"College degree\\n or higher\"), size=16)\n",
    "\n",
    "for i in range(0,13):\n",
    "    ax[1].axhline(i+0.5,  color='grey', alpha=0.25, linewidth=1) # linestyle=\":\",\n",
    "\n",
    "ax[1].fill_between([-0.25,1.25],8.5,12.5, alpha=0.05, color='black')\n",
    "ax[1].fill_between([-0.25,1.25],2.5,5.5, alpha=0.05, color='black')\n",
    "ax[1].fill_between([-0.25,1.25],-0.5,0.5, alpha=0.05, color='black')\n",
    "\n",
    "ax[1].axhline(12.5, color='black')\n",
    "ax[1].axhline(8.5, color='black')\n",
    "ax[1].axhline(5.5, color='black')\n",
    "ax[1].axhline(2.5, color='black')\n",
    "ax[1].axhline(0.5, color='black')\n",
    "\n",
    "ax[1].set_xlim(-0.1, 1.1)\n",
    "ax[1].set_ylim(-0.5,13.5)\n",
    "\n",
    "ax[1].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "ax[1].set_title(\"Kenya\", fontsize=18)\n",
    "ax[1].set_xlabel('Proportion of respondents', fontsize=14)\n",
    "\n",
    "########## Mexico #############################################################################################\n",
    "i=-0.3\n",
    "\n",
    "for var in ['Facebook Sample', \n",
    "            'Facebook Sample (weighted)', \n",
    "            'Facebook Population', \n",
    "            'LAPOP',  \n",
    "            'Census']:\n",
    "    \n",
    "    plot_df = mex_demo.loc[[\n",
    "                              'Divorced', 'Married', 'Single',    \n",
    "                              'No religion',  'Non-Catholic',  'Catholic',\n",
    "                              'Did not complete secondary school', 'Secondary degree', 'College degree or higher',\n",
    "                              '60+', '50-59', '30-49',  '18-29',\n",
    "                              'Female' ]][var].reset_index()\n",
    "    \n",
    "    ax[0].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8, label=var)\n",
    "    i+=0.15\n",
    "    \n",
    "ax[0].set_yticks(range(0, plot_df.shape[0]))\n",
    "ax[0].set_yticklabels(plot_df.variable.str.replace(\"Technical\", \"Tech.\").str\n",
    "                      .replace(\"higher\", \"above\")\n",
    "                      .replace(\"Did not complete secondary school\", \"Didn't complete\\nsecondary\")\n",
    "                      .replace(\"College degree or above\", \"College degree\\n or higher\"), \n",
    "                      size=16)\n",
    "\n",
    "for i in range(0,13):\n",
    "    ax[0].axhline(i+0.5,  color='grey', alpha=0.25, linewidth=1) # linestyle=\":\",\n",
    "\n",
    "ax[0].fill_between([-0.25,1.25],8.5,12.5, alpha=0.05, color='black')\n",
    "ax[0].fill_between([-0.25,1.25],2.5,5.5, alpha=0.05, color='black')\n",
    "\n",
    "\n",
    "ax[0].axhline(12.5, color='black')\n",
    "ax[0].axhline(8.5, color='black')\n",
    "ax[0].axhline(5.5, color='black')\n",
    "ax[0].axhline(2.5, color='black')\n",
    "\n",
    "ax[0].set_xlim(-0.1, 1.1)\n",
    "ax[0].set_ylim(-0.5,13.5)\n",
    "\n",
    "ax[0].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "ax[0].set_title(\"Mexico\", fontsize=18)\n",
    "ax[0].set_xlabel('Proportion of respondents', fontsize=14)\n",
    "\n",
    "########## Indonesia #############################################################################################\n",
    "i=-0.4\n",
    "\n",
    "for var in ['Facebook Sample',\n",
    "            'Facebook Sample (weighted)',\n",
    "            'Facebook Population', \n",
    "            'Asiabarometer',\n",
    "            'Census', \n",
    "             'Dynata', 'Dynata (weighted)'\n",
    "    ]:\n",
    "    \n",
    "    plot_df = ind_demo.loc[[ \n",
    "                            'Divorced', 'Married', 'Single',  \n",
    "                            'Other religion', 'Muslim','Christian', \n",
    "                              'Did not complete secondary school', 'Secondary degree', 'College degree or higher',  \n",
    "                            '60+','50-59','30-49', '18-29',\n",
    "                            'Female', \n",
    "        ]][var].reset_index()\n",
    "    \n",
    "    ax[2].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8,\n",
    "                  label=var.replace(\"Afrobarometer\", \"LAPOP/Afrobarometer/Asiabarometer\"))\n",
    "    i+=0.125\n",
    "\n",
    "    \n",
    "    \n",
    "ax[2].set_yticks(range(0, plot_df.shape[0]))\n",
    "ax[2].set_yticklabels(plot_df.variable.str.replace(\"schl\", \"school\")\n",
    "                      .replace(\"Did not complete secondary school\", \"Didn't complete \\nsecondary\")\n",
    "                      .replace(\"College degree or higher\", \"College degree\\n or higher\"),\n",
    "                      size=16)\n",
    "\n",
    "for i in range(0,13):\n",
    "    ax[2].axhline(i+0.5,  color='grey', alpha=0.25, linewidth=1) # linestyle=\":\",\n",
    "\n",
    "\n",
    "ax[2].fill_between([-0.25,1.25],12.5,8.5, alpha=0.05, color='black')\n",
    "ax[2].fill_between([-0.25,1.25],5.5,2.5, alpha=0.05, color='black')\n",
    "\n",
    "ax[2].axhline(12.5, color='black')\n",
    "ax[2].axhline(8.5, color='black')\n",
    "ax[2].axhline(5.5, color='black')\n",
    "ax[2].axhline(2.5, color='black')\n",
    "\n",
    "ax[2].set_xlim(-0.1, 1.1)\n",
    "ax[2].set_ylim(-0.5,13.5)\n",
    "\n",
    "ax[2].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "ax[2].set_title(\"Indonesia\", fontsize=18)\n",
    "ax[2].set_xlabel('Proportion of respondents', fontsize=14)\n",
    "\n",
    "\n",
    "##############\n",
    "p=[]\n",
    "for i in ['Facebook Sample', 'Dynata','Facebook Sample (weighted)', 'Dynata (weighted)', 'Facebook Population', \n",
    "        'LAPOP/Afrobarometer/Asiabarometer', 'Census', \n",
    "           ]:\n",
    "        patch = mlines.Line2D([], [], color=colors[i], marker=styles[i], linestyle='None',\n",
    "                          fillstyle=fill_styles[i], markersize=10, label=i)\n",
    "        p.append(patch)\n",
    "\n",
    "ax[1].legend(handles=p, loc='lower center', ncol=4, bbox_to_anchor=[0.45,-0.2], fontsize=14)\n",
    "\n",
    "\n",
    "ax[0].tick_params(axis='x', which='major', labelsize=14)\n",
    "ax[1].tick_params(axis='x', which='major', labelsize=14)\n",
    "ax[2].tick_params(axis='x', which='major', labelsize=14)\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.5, hspace=None)\n",
    "plt.savefig(\"figures/demos_for_paper_23.pdf\", bbox_inches='tight', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518d4592",
   "metadata": {},
   "source": [
    "### FIGURE 3: Non-response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e08238bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:04:59.246761Z",
     "iopub.status.busy": "2024-11-21T01:04:59.245236Z",
     "iopub.status.idle": "2024-11-21T01:05:08.721825Z",
     "shell.execute_reply": "2024-11-21T01:05:08.721825Z",
     "shell.execute_reply.started": "2024-11-21T01:04:59.246761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3, sharey = True, figsize=[10,3])\n",
    "\n",
    "# Kenya\n",
    "plot_df = kenya_regs.loc[['Male', 'Age 32+', ]]\n",
    "\n",
    "ax[1].errorbar(x= plot_df['estimate'], y = plot_df.index, xerr = 1.96*plot_df['std.error'], \n",
    "                 linestyle='None', \n",
    "                 marker='o', \n",
    "                 color='mediumseagreen')\n",
    "\n",
    "# Mexico\n",
    "plot_df = mexico_regs.loc[['Male', 'Age 60+', 'Age 50-59', 'Age 30-49', ]]\n",
    "\n",
    "ax[0].errorbar(x= plot_df['estimate'], y = plot_df.index, xerr = 1.96*plot_df['std.error'], \n",
    "                 linestyle='None', \n",
    "                 marker='o', \n",
    "                 color='mediumseagreen')\n",
    "\n",
    "# Indonesia\n",
    "plot_df = indonesia_regs.loc[['Male', 'Age 60+', 'Age 50-59', 'Age 30-49', ]]\n",
    "\n",
    "ax[2].errorbar(x= plot_df['estimate'], y = plot_df.index, xerr = 1.96*plot_df['std.error'], \n",
    "                 linestyle='None', \n",
    "                 marker='o', \n",
    "                 color='mediumseagreen')\n",
    "\n",
    "# Axis styling\n",
    "ax[0].axvline(0, color=\"grey\", linestyle=\":\")\n",
    "ax[0].set_xlim(-.2, .2)\n",
    "ax[0].set_title(\"Mexico\")\n",
    "\n",
    "ax[1].axvline(0, color=\"grey\", linestyle=\":\")\n",
    "ax[1].set_xlim(-.2, .2)\n",
    "ax[1].set_title(\"Kenya\")\n",
    "\n",
    "ax[2].axvline(0, color=\"grey\", linestyle=\":\")\n",
    "ax[2].set_xlim(-.2, .2)\n",
    "ax[2].set_title(\"Indonesia\")\n",
    "\n",
    "ax[0].set_ylim(-0.5,4.5)\n",
    "\n",
    "plt.savefig(\"figures/non_response.pdf\", bbox_inches='tight', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76dee8fa",
   "metadata": {},
   "source": [
    "### FIGURE 4: Attitudes (politics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80ca13d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:05:08.724959Z",
     "iopub.status.busy": "2024-11-21T01:05:08.724959Z",
     "iopub.status.idle": "2024-11-21T01:05:10.858242Z",
     "shell.execute_reply": "2024-11-21T01:05:10.858242Z",
     "shell.execute_reply.started": "2024-11-21T01:05:08.724959Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=[20,7])\n",
    "\n",
    "########## KENYA #############################################################################################\n",
    "i=-0.275\n",
    "\n",
    "for var in [ 'Election results', 'Facebook Sample', 'Facebook Sample (weighted)', 'Afrobarometer',]:\n",
    "    \n",
    "    plot_df = kenya_att.loc[[  \n",
    "                    'Approve of President', \n",
    "                                'Free to speak',\n",
    "                    'Close to political party', \n",
    "                    'Raised an issue', \n",
    "                    'Voted in 2017 election', \n",
    "                    'Attended meeting', \n",
    "                    'Protested'\n",
    "                             ]][var].reset_index()\n",
    "    \n",
    "\n",
    "    ax[0].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8,\n",
    "                  label=var, markersize=8)\n",
    "    i+=0.17\n",
    "    \n",
    "    \n",
    "ax[0].set_yticks(range(0, plot_df.shape[0]))\n",
    "ax[0].set_yticklabels(plot_df['variable'])\n",
    "\n",
    "for i in range(0,6):\n",
    "    ax[0].axhline(i+0.5,  color='grey', alpha=0.4, linewidth=1) \n",
    "    \n",
    "ax[0].fill_between([-0.25,1.25],2.5,6.5, alpha=0.05, color='black')\n",
    "ax[0].axhline(2.5, color='black')\n",
    "\n",
    "ax[0].set_xlim(-0.1, 1.1)\n",
    "ax[0].set_ylim(-0.5,6.5)\n",
    "\n",
    "ax[0].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "ax[0].set_xlabel(\"Proportion of respondents\", fontsize=17)\n",
    "\n",
    "ax[0].tick_params(labelsize=16)\n",
    "ax[0].set_title(\"Kenya\", fontsize=18)\n",
    "\n",
    "######## Indonesia ###############################################################################\n",
    "i=-0.375\n",
    "for var in [  'Election results','Facebook Sample','Facebook Sample (weighted)',  'Asiabarometer', 'Dynata', 'Dynata (weighted)' ]:\n",
    "    plot_df = ind_att.loc[[\n",
    "                            \"President's party (PDI-P)\", \n",
    "                            'Vote in most or every election',   \n",
    "                            'Signed a petition', 'Posted online', \n",
    "                            'Voted in 2019 election', \n",
    "                            'Attended a meeting', \n",
    "                            'Protested',\n",
    "                          ]][var].reset_index()\n",
    "    ax[1].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8,\n",
    "                  label=var,\n",
    "                markersize=8)\n",
    "    i+=0.15\n",
    "        \n",
    "ax[1].set_yticks(range(0, plot_df.shape[0]))\n",
    "\n",
    "for i in range(0,8):\n",
    "    ax[1].axhline(i+0.5,  color='grey', alpha=0.4, linewidth=1) \n",
    "\n",
    "ax[1].fill_between([-0.25,1.25],1.5,6.5, alpha=0.05, color='black')\n",
    "ax[1].axhline(1.5, color='black')\n",
    "\n",
    "ax[1].set_xlim(-0.1, 1.1)\n",
    "ax[1].set_ylim(-0.5,6.5)\n",
    "\n",
    "ax[1].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "#ax.set_title(\"Kenya\", fontsize=14)\n",
    "ax[1].set_xlabel(\"Proportion of respondents\", fontsize=17)\n",
    "ax[1].set_yticklabels(plot_df['variable'])\n",
    "ax[1].tick_params(labelsize=16)\n",
    "ax[1].set_title(\"Indonesia\", fontsize=18)\n",
    "#############################\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.6, hspace=None)\n",
    "p=[]\n",
    "for i in ['Facebook Sample', 'Facebook Sample (weighted)', 'Dynata','Dynata (weighted)', \n",
    "          'Afrobarometer/Asiabarometer',  \"Election results\"]:\n",
    "        patch = mlines.Line2D([], [], color=colors[i], marker=styles[i], linestyle='None',\n",
    "                          fillstyle=fill_styles[i], markersize=10, label=i)\n",
    "        p.append(patch)\n",
    "\n",
    "ax[1].legend(handles=p, loc='center right', ncol=4,  fontsize=16, bbox_to_anchor=[.7,-0.2])\n",
    "plt.savefig(\"figures/politics_kenya_indonesia.pdf\", bbox_inches='tight', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b6ff1c",
   "metadata": {},
   "source": [
    "### FIGURE 5: Attitudes (environment and climate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "953a9330",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:05:10.858242Z",
     "iopub.status.busy": "2024-11-21T01:05:10.858242Z",
     "iopub.status.idle": "2024-11-21T01:05:12.982919Z",
     "shell.execute_reply": "2024-11-21T01:05:12.982919Z",
     "shell.execute_reply.started": "2024-11-21T01:05:10.858242Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=[20,7])\n",
    "\n",
    "########## Mexico #############################################################################################\n",
    "i=-0.2\n",
    "\n",
    "\n",
    "for var in ['Facebook Sample', 'Facebook Sample (weighted)', 'LAPOP']:\n",
    "    \n",
    "    plot_df = mex_att[var].reset_index()\n",
    "    \n",
    "    ax[0].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8,\n",
    "                  label=var,\n",
    "               markersize=8)\n",
    "    i+=0.175\n",
    "\n",
    "    \n",
    "    \n",
    "ax[0].set_yticks(range(0, plot_df.shape[0]))\n",
    "ax[0].set_yticklabels(plot_df['variable'], size=16)\n",
    "\n",
    "for i in range(0,6):\n",
    "    ax[0].axhline(i+0.5,  color='grey', alpha=0.4, linewidth=1) # linestyle=\":\",\n",
    "\n",
    "ax[0].set_xlim(-0.1, 1.1)\n",
    "ax[0].set_ylim(-0.5,6.5)\n",
    "\n",
    "\n",
    "ax[0].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "ax[0].set_xlabel(\"Proportion of respondents\", fontsize=17)\n",
    "ax[0].set_ylabel(\"$\\longleftarrow$ Environment $\\quad\\quad\\quad\\quad\\quad\\quad$ Economic Growth $\\longrightarrow$\", fontsize=17)\n",
    "ax[0].tick_params(labelsize=16)\n",
    "ax[0].set_title(\"Mexico\", fontsize=18)\n",
    "\n",
    "########## Indonesia #############################################################################################\n",
    "i=-0.3\n",
    "\n",
    "for var in [ 'Facebook Sample', 'Facebook Sample (weighted)', 'Dynata', 'Dynata (weighted)' ]:\n",
    "    plot_df = ind_att.loc[[ \n",
    "       'Climate change is happening', 'Climate change is human caused',\n",
    "       'Climate change not at all important',\n",
    "       'Climate change not too important', \n",
    "        'Climate change somewhat important',\n",
    "       'Climate change very important',\n",
    "        'Climate change extremely important',\n",
    "                          ]][var].reset_index()\n",
    "    \n",
    "    ax[1].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8,\n",
    "                  label=var, \n",
    "                markersize=8)\n",
    "    i+=0.2\n",
    "\n",
    "ax[1].set_yticks(range(0, plot_df.shape[0]))\n",
    "\n",
    "for i in range(0,8):\n",
    "    ax[1].axhline(i+0.5,  color='grey', alpha=0.4, linewidth=1) # linestyle=\":\",\n",
    "\n",
    "ax[1].fill_between([-0.25,1.25],1.5,6.5, alpha=0.05, color='black')\n",
    "ax[1].axhline(1.5, color='black')\n",
    "\n",
    "ax[1].set_xlim(-0.1, 1.1)\n",
    "ax[1].set_ylim(-0.5,6.5)\n",
    "\n",
    "ax[1].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "ax[1].set_xlabel(\"Proportion of respondents\", fontsize=17)\n",
    "ax[1].set_yticklabels(plot_df['variable'])\n",
    "ax[1].legend(loc='center right', ncol=2,  fontsize=16, bbox_to_anchor=[1,-0.2])\n",
    "ax[1].tick_params(labelsize=16)\n",
    "\n",
    "ax[1].set_title(\"Indonesia\", fontsize=18)\n",
    "\n",
    "#############################\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.6, hspace=None)\n",
    "p=[]\n",
    "for i in [\"Facebook Sample\", \"Facebook Sample (weighted)\", \"Dynata\", \"Dynata (weighted)\", \"LAPOP\"]:\n",
    "        patch = mlines.Line2D([], [], color=colors[i], marker=styles[i], linestyle='None',\n",
    "                          fillstyle=fill_styles[i], markersize=10, label=i)\n",
    "        p.append(patch)\n",
    "\n",
    "ax[1].legend(handles=p, loc='center right', ncol=3,  fontsize=16, bbox_to_anchor=[.25,-0.2])\n",
    "plt.savefig(\"figures/environment_mexico_indonesia.pdf\", bbox_inches='tight', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce272e2f",
   "metadata": {},
   "source": [
    "### FIGURE S1: Sampling Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "897e318b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-21T01:05:12.982919Z",
     "iopub.status.busy": "2024-11-21T01:05:12.982919Z",
     "iopub.status.idle": "2024-11-21T01:05:14.812635Z",
     "shell.execute_reply": "2024-11-21T01:05:14.809336Z",
     "shell.execute_reply.started": "2024-11-21T01:05:12.982919Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3, figsize=[15,7])\n",
    "\n",
    "########## KENYA #############################################################################################\n",
    "\n",
    "i=0.05\n",
    "\n",
    "# This has to be plotted separately because Ad_female label is different -- later overwritten by population labels\n",
    "for var in ['Facebook (ad clickers)']:    \n",
    "\n",
    "    plot_df = kenya_demo.loc[['60+', '50-59', '30-49','18-29', \n",
    "                              'Ad_Female' ]][var].reset_index()\n",
    "    \n",
    "    ax[1].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8,)\n",
    "    i-=0.1    \n",
    "    \n",
    "for var in ['Facebook Population',]:\n",
    "\n",
    "    plot_df = kenya_demo.loc[['60+', '50-59', '30-49',  '18-29', #'32+', \n",
    "                              'Female' ]][var].reset_index()\n",
    "    \n",
    "    ax[1].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8,)\n",
    "    i+=0.15\n",
    "    \n",
    "    \n",
    "ax[1].set_yticks(range(0, plot_df.shape[0]))\n",
    "ax[1].set_yticklabels(plot_df.variable, size=16)\n",
    "\n",
    "for i in range(0,4):\n",
    "    ax[1].axhline(i+0.5,  color='grey', alpha=0.25, linewidth=1)\n",
    "\n",
    "ax[1].fill_between([-0.25,1.25],3.5,4.5, alpha=0.05, color='black')\n",
    "\n",
    "ax[1].axhline(3.5, color='black')\n",
    "\n",
    "ax[1].set_xlim(0, .7)\n",
    "ax[1].set_ylim(-0.5,4.5)\n",
    "\n",
    "\n",
    "ax[1].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "\n",
    "ax[1].set_title(\"Kenya\", fontsize=14)\n",
    "ax[1].set_xlabel('Proportion of respondents', fontsize=12)\n",
    "\n",
    "########## Mexico #############################################################################################\n",
    "i=-0.05\n",
    "\n",
    "for var in ['Facebook Population','Facebook (ad clickers)', ]:\n",
    "    \n",
    "    plot_df = mex_demo.loc[[ '60+', '50-59', '30-49',  '18-29','Female' ]][var].reset_index()\n",
    "    \n",
    "    ax[0].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8, label=var)\n",
    "    i+=0.1\n",
    "    \n",
    "    \n",
    "ax[0].set_yticks(range(0, plot_df.shape[0]))\n",
    "ax[0].set_yticklabels(plot_df.variable, size=16)\n",
    "\n",
    "for i in range(0,4):\n",
    "    ax[0].axhline(i+0.5,  color='grey', alpha=0.25, linewidth=1) # linestyle=\":\",\n",
    "\n",
    "\n",
    "ax[0].fill_between([-0.25,1.25],3.5,4.5, alpha=0.05, color='black')\n",
    "\n",
    "ax[0].axhline(3.5, color='black')\n",
    "\n",
    "\n",
    "ax[0].set_xlim(0, .7)\n",
    "ax[0].set_ylim(-0.5,4.5)\n",
    "\n",
    "ax[0].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "ax[0].set_title(\"Mexico\", fontsize=14)\n",
    "ax[0].set_xlabel('Proportion of respondents', fontsize=12)\n",
    "\n",
    "########## Indonesia #############################################################################################\n",
    "i=0.05\n",
    "\n",
    "# This has to be plotted separately because range is 21-29 --> later overwritten by population labels\n",
    "for var in ['Facebook (ad clickers)',]:\n",
    "    \n",
    "    plot_df = ind_demo.loc[['60+','50-59','30-49', '21-29','Female', \n",
    "        ]][var].reset_index()\n",
    "    \n",
    "    ax[2].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8)\n",
    "    \n",
    "    i-=0.1\n",
    "\n",
    "for var in ['Facebook Population',]:\n",
    "    \n",
    "    plot_df = ind_demo.loc[[ '60+','50-59','30-49', '18-29', 'Female', \n",
    "        ]][var].reset_index()\n",
    "    \n",
    "    ax[2].errorbar(x= plot_df['mean'], y = plot_df.index-i, xerr = 1.96*plot_df['se'], \n",
    "                 linestyle='None', \n",
    "                 marker=styles[var], \n",
    "                 color=colors[var], \n",
    "                 fillstyle=fill_styles[var],\n",
    "                 alpha=0.8)\n",
    "\n",
    "    \n",
    "ax[2].set_yticks(range(0, plot_df.shape[0]))\n",
    "ax[2].set_yticklabels(plot_df.variable, size=16)\n",
    "\n",
    "for i in range(0,4):\n",
    "    ax[2].axhline(i+0.5,  color='grey', alpha=0.25, linewidth=1) \n",
    "\n",
    "ax[2].fill_between([-0.25,1.25],3.5,4.5, alpha=0.05, color='black')\n",
    "\n",
    "ax[2].axhline(3.5, color='black')\n",
    "\n",
    "ax[2].set_xlim(0, .7)\n",
    "ax[2].set_ylim(-0.5,4.5)\n",
    "\n",
    "ax[2].grid(which='major', axis='x', alpha=0.25)\n",
    "\n",
    "ax[2].set_title(\"Indonesia\", fontsize=14)\n",
    "ax[2].set_xlabel('Proportion of respondents', fontsize=12)\n",
    "\n",
    "\n",
    "##############\n",
    "p=[]\n",
    "for i in ['Facebook (ad clickers)',\n",
    "            'Facebook Population',\n",
    "            #'Census\n",
    "         ]:\n",
    "        patch = mlines.Line2D([], [], color=colors[i], marker=styles[i], linestyle='None',\n",
    "                          fillstyle=fill_styles[i], markersize=10, label=i)\n",
    "        p.append(patch)\n",
    "\n",
    "ax[1].legend(handles=p, loc='lower center', ncol=3, bbox_to_anchor=[0.45,-0.2], fontsize=12)\n",
    "\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.5, hspace=None)\n",
    "plt.savefig(\"figures/sampling_error.pdf\", bbox_inches='tight', dpi=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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